This issue is well-understood [1]}, and several potential solutions have been proposed.
For somatic imaging it makes sense to restrict the support of \(\mathbf {A}\)  to remain close to their initial values (we could use a dilation of the superpixel support for this). But for data with large dendritic or axonal components this approach would cut off large fractions of these components. Sparse regression updates are an option here, but these do not enforce spatial structure in the resulting \(\mathbf {A}\)  directly; this often results in “speckle" noise in the estimated spatial components (c.f. Figure REF  below).
